Sensor networks are used in a variety of applications for event monitoring, environmental sensing and outer space exploration. An important application is detecting a target in the field using sensors gathering acoustic data. In this target detection application (ATR), a cluster of wireless sensors collected acoustic data and perform signal processing. In the algorithm used for signal processing, acoustic data collected by the sensors need to be communicated to a designated head node for determining the target direction of bearing. The data collected by geometrically closely distributed sensors show high spatial correlation. In this paper, our focus is on energy efficient coding schemes for wireless sensor networks. First we give an analysis to show why conventional compression scheme give poor performance when energy consumption for encoding and decoding processing overheads are considered. We then describe a new coding scheme called EEADSC, which minimizes the Lagrangian cost function. The proposed scheme fully exploits spatial correlation in wireless sensor network and is adaptive according to tracking signal strength. We evaluated the proposed scheme using datasets from an ATR application, which achieved up to a factor of 8 data compression. EEADSC uses TCQ quantization and trellis encoding to represent a 16 bit data value by as few as 2 bits. With the scheme, we reduce the overall energy cost for communication in this application by a factor of 2.53, including the overhead processing cost in encoding/decoding. The scheme also fits well for general sensor network applications in which some data collection and aggregation are performed.